Color Image Segmentation Method Research Articles

A color image segmentation method as used in the study of ancient monument decay

In the cultural heritage area, it is of fundamental importance to characterize and classify the conservation state of the materials constituting ancient monuments, in order to study and monitor their decay. Generally, the decay diagnosis is provided by “naked eye” analysis done by expert scientists “walking around” the artifact and recording the conservation state of each individual element they observe. In this paper, a color image segmentation approach, based on histogram threshold and edge detection techniques is presented, to extract degradation regions, characterized by holes or cavities, from color images of stone-materials. The goal is to provide an aid to the decay diagnosis by segmenting degraded regions from color images, computing quantitative data, such as the area and perimeter of the extracted zones, and processing qualitative information, such as various levels of depth detected into the same zones. Since color is a powerful tool in the distinction between objects, a segmentation technique based on color, instead of intensity only, has been used to provide a clearer discrimination between regions. The study case concerns the impressive remains of the Roman Theatre in the city of Aosta (Italy). In particular, we have processed and analyzed some color images of the theatre puddingstones, acquired by a camera.

Color image segmentation based on three levels of texture statistical evaluation

In this paper a new and efficient supervised method for color image segmentation is presented. This method improves a part of the automatic extraction problem. The basic technique consists in fusing information streaming from three different sources for the same image. The first source uses information coming from only one pixel, using the Mahalanobis distance. The second uses the multidimensional distribution of the three bands in a window centered in each pixel, using the Bhattacharyya distance. And the third employs cooccurrence matrices over the texture cube built around one pixel, using the Bhattacharyya distance again. The Dempster–Shafer theory of evidence is applied in order to fuse the information from the three sources which represent different orders of statistics. This method reveals the importance of applying context and textural properties for the segmentation process. The results show the potential of the method for real images starting from the three RGB bands only.

An unsupervised color image segmentation based on morphological 2D clustering and fusion

A segmentation method for color images is presented in this work. A morphological unsupervised 2D multiband histogram clustering provides an initial coarse segmntation of the image. Region information is then used and a novel technique is introduced to simplify the Region Adjacency Graph by merging candidate regions until the stabilization of a segmentation criterion. Merged regions are refined by a color watershed. The whole method requires the definition of only one single parameter which acts as a kind of multiscale parameter.

A four-stage system for blind colour image segmentation

This paper proposes a new method to split colour images into regions. The only input information is the image to be segmented. Hence, this is a blind colour image segmentation method. It consists of four subsystems: preprocessing, cluster detection,

オンライン学習機能を持つ視覚追跡システムの提案

In order to keep visual tracking systems with color segmentation technique running in real environment, it should be developed on-line learning method to update models for adapting them to dynamic changes of surroundings.To deal with this problem, we propose an on-line visual learning method for color image segmentation and object tracking in dynamic environment. Our method utilizes Fuzzy ART architecture which is a kind of neural network for competitive learning. The mechanism of this neural network is suitable for on-line learning and different from that of backpropagation type neural network. In order to use Fuzzy ART architecture for color segmentation on-line, we transform the color signal that the framegrabber used yields to a particular color space called Yrθ space. To show validity of our method, we present some results of experiments using sequences of real images.

Color Image Segmentation for Multimedia Applications

Image segmentation is crucial for multimedia applications. Multimedia databases utilize segmentation for the storage and indexing of images and video. Image segmentation is used for object tracking in the new MPEG-7 video compression standard. It is also used in video conferencing for compression and coding purposes. These are only some of the multimedia applications in image segmentation. It is usually the first task of any image analysis process, and thus, subsequent tasks rely heavily on the quality of segmentation. The proposed method of color image segmentation is very effective in segmenting a multimedia-type image into regions. Pixels are first classified as either chromatic or achromatic depending on their HSI color values. Next, a seed determination algorithm finds seed pixels that are in the center of regions. These seed pixels are used in the region growing step to grow regions by comparing these seed pixels to neighboring pixels using the cylindrical distance metric. Merging regions that are similar in color is a final means used for segmenting the image into even smaller regions.

Color image segmentation for Bladder Cancer Diagnosis

In this paper, a new color image segmentation method is proposed to extract the region of bladder tumor from a color bladder image. In the past, the diagnosis of bladder tumors mainly relies upon cystoscopic examination with an in vivo staining technique. This manner heavily depends on the interpreter's experience and often results in errors in diagnosis. Instead of previous method, we use methylene blue in vivo staining combined with color segmentation techniques to improve the accuracy of the diagnosis of bladder tumors. The segmentation task can be decomposed into two stages. First, cluster detection combined with probabilistic relaxation is used to extract the clusters of specified colors from the HLS color space. Then, in order to refine the chromatic properties, the Bayesian algorithm is employed to reject the false region from the clusters obtained in the first stage. Experimental results show that the proposed method can segment the bladder tumor successfully. The technique could serve as an auxiliary tool for doctors or researchers in the diagnosis of bladder tumors.

Fusion of color and edge information for improved segmentation and edge linking

We propose a new method for combined color image segmentation and edge linking. The image is first segmented based on color information only. The segmentation map is modeled by a Gibbs random field, to ensure formation of spatially contiguous regions. Next, spatial edge locations are determined using the magnitude of the gradient of the 3-channel image vector field. Finally, regions in the segmentation map are split and merged by a region-labeling procedure to enforce their consistency with the edge map. The boundaries of the final segmentation map constitute a linked edge map. Experimental results are reported.

Color image segmentation using competitive learning

Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results. >

Automatic color segmentation algorithms-with application to skin tumor feature identification

Two color-image segmentation methods are described. The first is based on a spherical coordinate transform of original RGB data. The second is based on a mathematically optimal transform, the principal components transform (also known as eigenvector, discrete Karhunen-Loeve, or Hotelling transform). These algorithms are applied to the extraction from skin tumor images of various features such as tumor border, crust, hair scale, shiny areas, and ulcer. The results of this research will be used in the development of a computer vision system that will seve as the visual front-end of a medical expert system to automate visual feature identification for skin tumor evaluation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>